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European signal processing Conference

作者： Nobutaka Ito Shoko Araki Tomohiro Nakatani NTT Communication Science Laboratories NTT Corporation

ISBN: (纸本)9781509018918

microphone array signal processing based on time-frequency masks has been applied successfully to various tasks including source separation, denoising, source localization, and source counting. Aiming to improve the performance of these techniques, here we propose a mask estimation method based on a complex Angular Central Gaussian Mixture Model (cACGMM) for multichannel observed signals. Compared to a conventional complex Watson Mixture Model (cWMM), the proposed cACGMM can model not only rotationally symmetrical but also elliptical distributions. Therefore, the cACGMM can better approximate the distribution of observed data, which is generally not rotationally symmetrical. In source separation simulations with real recorded impulse responses, the cACGMM resulted in an average 1.2 dB improvement of the signal-to-Distortion Ratio (SDR) over the cWMM.

关键词： Time-frequency masks microphone array signal processing Complex angular central Gaussian distributions signal processing Mixture models Source separation microphone Device Component DIRECTIONAL Position measurement Gauss Phase Mask Statistical analysis signal-to-distortion ratio observational data Masks Religious Missions

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Joint Maximum Likelihood Estimation of microphone array Parameters for a Reverberant Single Source Scenario

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 695-705页

作者： Li, Changheng Martinez, Jorge Hendriks, Richard Christian Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands

Estimation of the acoustic-scene related parameters such as relative transfer functions (RTFs) from source to microphones, source power spectral densities (PSDs) and PSDs of the late reverberation is essential and also challenging. Existing maximum likelihood estimators typically consider only subsets of these parameters and use each time frame separately. In this paper we explicitly focus on the single source scenario and first propose a joint maximum likelihood estimator (MLE) to estimate all parameters jointly using a single time frame. Since the RTFs are typically invariant for a number of consecutive time frames we also propose a joint maximum likelihood estimator (MLE) using multiple time frames which has similar estimation performance compared to a recently proposed reference algorithm called simultaneously confirmatory factor analysis (SCFA), but at a much lower complexity. Moreover, we present experimental results which demonstrate that the estimation accuracy, together with the performance of noise reduction, speech quality and speech intelligibility, of our proposed joint MLE outperform those of existing MLE based approaches that use only a single time frame.

关键词： Maximum likelihood estimation array signal processing Noise reduction Transfer functions signal processing algorithms Parallel processing microphone arrays Dereverberation maximum likelihood estima- tion microphone array signal processing PSD estimation RTF estimation

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Alternating Least-Squares-Based microphone array Parameter Estimation for a Single-Source Reverberant and Noisy Acoustic Scenario

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 3922-3934页

作者： Li, Changheng Hendriks, Richard C. Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands

Acoustic-scene-related parameters such as relative transfer functions (RTFs) and power spectral densities (PSDs) of the target source, late reverberation and ambient noise are essential for microphone array signal processing and are challenging to estimate. Existing methods typically only estimate a subset of the parameters by assuming the other parameters are known. This can lead to unmatched scenarios and reduced estimation performance on the parameters of interest. Moreover, many methods process time frames independently, despite they share common information such as the same RTF. In this work, we consider a noisy scenario by modelling the noise component as a spatially homogeneous sound field with a time-invariant spatial coherence matrix and time-varying PSD. We first modify an existing alternating least squares (ALS) method to obtain more accurate estimates using a single time frame. Then, we extend the method to use multiple time frames that share the same RTF. Furthermore, we propose more robust constraints on the PSDs to avoid large estimation errors. We compare our proposed methods to the state-of-the-art simultaneously confirmatory factor analysis (SCFA) method, a joint maximum likelihood estimation (JMLE) method and an existing ALS-based method. The experimental results in terms of estimation accuracy, noise reduction performance, predicted speech quality, and predicted speech intelligibility demonstrate that our proposed methods achieve similar performance compared to the state-of-the-art SCFA method, which outperforms the existing ALS method in all scenarios and outperforms the JMLE method particularly in low SNR scenarios. Moreover, our proposed methods have significantly lower computational complexity than SCFA.

关键词： Reverberation Noise measurement microphone arrays Transfer functions Time-frequency analysis Spatial coherence Reflection Dereverberation noise reduction microphone array signal processing RTF estimation PSD estimation

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Acoustic Source Localization in the Circular Harmonic Domain Using Deep Learning Architecture

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 2475-2491页

作者： SongGong, Kunkun Wang, Wenwu Chen, Huawei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

The problem of direction of arrival (DOA) estimation with a circular microphone array has been addressed with classical source localization methods, such as the model-based methods and the param etric methods. These methods have an advantage in estimating the DOAs in a blind manner, i.e. with no (or limited) prior knowledge about the sound sources. However, their performance tends to degrade rapidly in noisy and reverberant environments or in the presence of sensor array limitations, such as sensor gain and phase errors. In this paper, we present a new approach by leveraging the strength of a convolutional neural network (CNN)-based deep learning approach. In particular, we design new circular harmonic features that are frequency-invariant as inputs to the CNN architecture, so as to offer improvements in DOA estimation in unseen adverse environments and obtain good adaptation to array imperfections. To our knowledge, such a deep learning approach has not been used in the circular harmonic domain. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than the recent baseline methods, in a variety of noise and reverberation levels, in terms of the accuracy of the DOA estimation.

关键词： Harmonic analysis Direction-of-arrival estimation Location awareness Estimation Sensor arrays Reverberation Deep learning Acoustic source localization circular harmonic convolutional neural network (CNN) deep learning architecture microphone array signal processing

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ADAPTIVE TIME SEGMENTATION FOR IMPROVED signal MODEL PARAMETER ESTIMATION FOR A SINGLE-SOURCE SCENARIO 57

ADAPTIVE TIME SEGMENTATION FOR IMPROVED SIGNAL MODEL PARAMET...

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57th Asilomar Conference on signals, Systems and Computers

作者： Li, Changheng Hendriks, Richard C. Delft Univ Technol Signal Proc Syst SPS Grp Delft Netherlands

ISBN: (纸本)9798350325744

Estimating the parameters that describe the acoustic scene is very important for many microphone array applications. For example, consider the power spectral densities (PSDs) or relative acoustic transfer functions (RTFs) that are required when estimating a particular sound source using multi-microphone noise reduction. State-of-the-art algorithms estimate the parameters per segment, where each segment consists of a fixed number of time frames. These algorithms exploit the assumption that PSDs are constant per time frame, and RTFs are constant per segment. However, in practice, sound sources will move relative to the microphone array. Improved performance is therefore expected when the actual time frames that are used to form the segments are adapted such that time frames all share the same (unknown) RTF. In this paper, we therefore present an algorithm to obtain an optimal adaptive time segmentation and combine this with our previously published joint maximum likelihood estimator (JMLE) for jointly estimating the RTF, source PSD and late reverberation PSD of a single source in a reverberant environment.

关键词： Adaptive time segmentation microphone array signal processing RTF estimation PSD estimation

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LOW COMPLEX ACCURATE MULTI-SOURCE RTF ESTIMATION 47

LOW COMPLEX ACCURATE MULTI-SOURCE RTF ESTIMATION

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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Changheng Martinez, Jorge Hendriks, Richard C. Delft Univ Technol Circuits & Syst CAS Grp Delft Netherlands

ISBN: (纸本)9781665405409

Many multi-microphone algorithms depend on knowing the relative acoustic transfer functions (RTFs) of the individual sound sources in the acoustic scene. However, accurate joint RTF estimation for multiple sources is a challenging problem. Existing methods to jointly estimate the RTF for multiple sources have either no satisfying performance, or, suffer from a very large computational complexity. In this paper, we propose a method for robust estimation of the individual RTFs in a multi-source acoustic scenario. The presented algorithm is based on linear algebraic concepts and therefore of lower computational complexity compared to a recently presented state-of-the-art algorithm, while having a similar performance. Experimental results are presented to demonstrate the RTF estimation performance as well as the noise reduction performance when combining the estimated RTFs with a beamformer.

关键词： Joint diagonalization microphone array signal processing source separation RTF estimation speech enhancement

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Indoor Multi-Speaker Localization Based on Bayesian Nonparametrics in the Circular Harmonic Domain

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2021年 29卷 1864-1880页

作者： SongGong, Kunkun Chen, Huawei Wang, Wenwu Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Circular microphone arrays have been used for multi-speaker localization in computational auditory scene analysis, for their high flexibility in sound field analysis, including the generation of frequency-invariant eigenbeams for wideband acoustic sources. However, the localization performance of existing circular harmonic approaches, such as circular harmonics beamformer (CHB) depends strongly on the physical characteristics (such as shape) of sensor arrays, and the level of uncertainties presented in acoustic environments (such as background noise, room reverberation, and the number of sources). These uncertainties may limit the performance or practical application of the speaker localization algorithms. To address these issues, in this paper, we present a new indoor multi-speaker localization method in the circular harmonic domain based on the acoustic holography beamforming (AHB) technique and the Bayesian nonparametrics (BNP) method. More specifically, we use the AHB technique, which combines the delay-and-sum beamforming with acoustic-holography-based virtual sensing, to generate direction of arrival (DOA) measurements in the time-frequency (TF) domain, and then design a BNP algorithm based on the infinite Gaussian mixture model (IGMM) to estimate the DOAs of the individual sources without the prior knowledge about the number of sources. These estimates may degrade in the presence of room reverberation and background noise. To address this issue, we develop a robust TF bin selection and permutation method on the basis of mixture weights, using power, power ratio and local variance estimated at each TF bin. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than four recent baseline methods, in a variety of noise and reverberation levels, in terms of the root-mean-square error (RMSE) of the DOA estimation and the source detecting success rate.

关键词： Direction-of-arrival estimation Location awareness Estimation Harmonic analysis Reverberation array signal processing Sensor arrays Multi-speaker localization Bayesian nonparametrics (BNP) circular harmonics direction of arrival (DOA) estimation microphone array signal processing

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Robust Indoor Speaker Localization in the Circular Harmonic Domain

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第4期68卷 3413-3422页

作者： SongGong, Kunkun Chen, Huawei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

In this article, modal signal processing using circular sensor arrays has been shown to be an attractive way for speaker localization, due to the fact that it enables high flexibility in sound field analysis, which inherently supports wideband acoustic sources and provides a frequency invariant beampattern. However, the existing circular harmonic direction-of-arrival (DOA) estimation approaches suffer from the following problems: first, depending strongly on the characteristics of sensor arrays;second, the presence of singularity when using Bessel function of the first kind;and third, being sensitive to noisy and reverberant environments. To combat these problems, this article proposes a robust indoor speaker localization method that incorporates circular harmonic pseudointensity vector through joining the least-squares decomposition and the spatial processing with a small-sized sensor array. The root mean square error of DOA estimation of the proposed method is compared with those published approaches in simulations for different noise levels and reverberation times. In addition, the superior performance of the proposed indoor speaker localization method is validated by using speech recordings in a real-world acoustic environment.

关键词： Harmonic analysis Sensor arrays array signal processing Estimation Direction-of-arrival estimation Robot sensing systems Acoustics Circular harmonic circular microphone array direction of arrival (DOA) estimation microphone array signal processing speaker localization

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Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

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IEEE signal processing LETTERS 2020年 27卷 920-924页

作者： Gong, Yuan Yang, Jian Poellabauer, Christian Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46637 USA

With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.

关键词： microphone arrays Task analysis Feature extraction Speech recognition array signal processing Convolution microphone array signal processing voice anti-spoofing replay attack beamforming

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EVALUATION OF JOINT AUDITORY ATTENTION DECODING AND ADAPTIVE BINAURAL BEAMFORMING APPROACH FOR HEARING DEVICES WITH ATTENTION SWITCHING

EVALUATION OF JOINT AUDITORY ATTENTION DECODING AND ADAPTIVE...

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IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Pu, Wenqiang Zan, Peng Xiao, Jinjun Zhang, Tao Luo, Zhi-Quan Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China Univ Maryland College Pk MD 20742 USA Starkey Hearing Technol Eden Prairie MN USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional hearing aids beamforming algorithms require the a priori knowledge of the auditory of the listener, which may not be available in real applications. Recent advances in electroencephalography (EEG) offer a potential non-invasive solution to this problem. The listener's auditory is derived from the EEG signals through auditory decoding algorithms and can be used as an input to the beamforming algorithms. In [1], a joint auditory decoding and adaptive beamforming algorithm framework by correlating the envelope of beamforming output and the EEG signal was proposed to improve the beamformer's robustness against decoding error. Consistent performance improvement was demonstrated on an EEG database recorded on listeners with fixed. In this study, we present the evaluation results of this joint formulation on a new EEG dataset collected on subjects with dynamic switch. We demonstrate not only the joint framework's performance improvement against decoding errors, but also its ability to capture listener's dynamic switch.

关键词： EEG signals auditory microphone array signal processing acoustic beamforming

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